{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Riskfolio-Lib Tutorial: \n",
    "<br>__[Financionerioncios](https://financioneroncios.wordpress.com)__\n",
    "<br>__[Orenji](https://www.orenj-i.net)__\n",
    "<br>__[Riskfolio-Lib](https://riskfolio-lib.readthedocs.io/en/latest/)__\n",
    "<br>__[Dany Cajas](https://www.linkedin.com/in/dany-cajas/)__\n",
    "<a href='https://ko-fi.com/B0B833SXD' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://cdn.ko-fi.com/cdn/kofi1.png?v=2' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> \n",
    "\n",
    "## Tutorial 30: Nested Clustered Optimization (NCO)\n",
    "\n",
    "## 1. Downloading the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  25 of 25 completed\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.options.display.float_format = '{:.4%}'.format\n",
    "\n",
    "# Date range\n",
    "start = '2016-01-01'\n",
    "end = '2019-12-30'\n",
    "\n",
    "# Tickers of assets\n",
    "assets = ['JCI', 'TGT', 'CMCSA', 'CPB', 'MO', 'APA', 'MMC', 'JPM',\n",
    "          'ZION', 'PSA', 'BAX', 'BMY', 'LUV', 'PCAR', 'TXT', 'TMO',\n",
    "          'DE', 'MSFT', 'HPQ', 'SEE', 'VZ', 'CNP', 'NI', 'T', 'BA']\n",
    "assets.sort()\n",
    "\n",
    "# Downloading data\n",
    "data = yf.download(assets, start = start, end = end)\n",
    "data = data.loc[:,('Adj Close', slice(None))]\n",
    "data.columns = assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-2.0257%</td>\n",
       "      <td>0.4057%</td>\n",
       "      <td>0.4036%</td>\n",
       "      <td>1.9693%</td>\n",
       "      <td>0.0180%</td>\n",
       "      <td>0.9305%</td>\n",
       "      <td>0.3678%</td>\n",
       "      <td>0.5783%</td>\n",
       "      <td>0.9483%</td>\n",
       "      <td>-1.1953%</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5881%</td>\n",
       "      <td>0.0212%</td>\n",
       "      <td>2.8236%</td>\n",
       "      <td>0.9758%</td>\n",
       "      <td>0.6987%</td>\n",
       "      <td>1.7539%</td>\n",
       "      <td>-0.1730%</td>\n",
       "      <td>0.2409%</td>\n",
       "      <td>1.3735%</td>\n",
       "      <td>-1.0857%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>-11.4863%</td>\n",
       "      <td>-1.5879%</td>\n",
       "      <td>0.2412%</td>\n",
       "      <td>-1.7557%</td>\n",
       "      <td>-0.7727%</td>\n",
       "      <td>-1.2473%</td>\n",
       "      <td>-0.1736%</td>\n",
       "      <td>-1.1238%</td>\n",
       "      <td>-3.5867%</td>\n",
       "      <td>-0.9551%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5547%</td>\n",
       "      <td>0.0212%</td>\n",
       "      <td>0.1592%</td>\n",
       "      <td>-1.5646%</td>\n",
       "      <td>0.3108%</td>\n",
       "      <td>-1.0155%</td>\n",
       "      <td>-0.7652%</td>\n",
       "      <td>-3.0048%</td>\n",
       "      <td>-0.9035%</td>\n",
       "      <td>-2.9145%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>-5.1389%</td>\n",
       "      <td>-4.1922%</td>\n",
       "      <td>-1.6573%</td>\n",
       "      <td>-2.7699%</td>\n",
       "      <td>-1.1047%</td>\n",
       "      <td>-1.9769%</td>\n",
       "      <td>-1.2206%</td>\n",
       "      <td>-0.8856%</td>\n",
       "      <td>-4.6058%</td>\n",
       "      <td>-2.5394%</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.2067%</td>\n",
       "      <td>-3.0310%</td>\n",
       "      <td>-1.0410%</td>\n",
       "      <td>-3.1557%</td>\n",
       "      <td>-1.6148%</td>\n",
       "      <td>-0.2700%</td>\n",
       "      <td>-2.2845%</td>\n",
       "      <td>-2.0570%</td>\n",
       "      <td>-0.5492%</td>\n",
       "      <td>-3.0019%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>0.2736%</td>\n",
       "      <td>-2.2705%</td>\n",
       "      <td>-1.6037%</td>\n",
       "      <td>-2.5425%</td>\n",
       "      <td>0.1099%</td>\n",
       "      <td>-0.2241%</td>\n",
       "      <td>0.5706%</td>\n",
       "      <td>-1.6402%</td>\n",
       "      <td>-1.7642%</td>\n",
       "      <td>-0.1649%</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1538%</td>\n",
       "      <td>-1.1366%</td>\n",
       "      <td>-0.7308%</td>\n",
       "      <td>-0.1449%</td>\n",
       "      <td>0.0895%</td>\n",
       "      <td>-3.3839%</td>\n",
       "      <td>-0.1117%</td>\n",
       "      <td>-1.1387%</td>\n",
       "      <td>-0.9720%</td>\n",
       "      <td>-1.1254%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>-4.3383%</td>\n",
       "      <td>0.1693%</td>\n",
       "      <td>-1.6851%</td>\n",
       "      <td>-1.0215%</td>\n",
       "      <td>0.0915%</td>\n",
       "      <td>-1.1791%</td>\n",
       "      <td>0.5674%</td>\n",
       "      <td>0.5288%</td>\n",
       "      <td>0.6616%</td>\n",
       "      <td>0.0330%</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6436%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.9869%</td>\n",
       "      <td>-0.1450%</td>\n",
       "      <td>1.2224%</td>\n",
       "      <td>1.4570%</td>\n",
       "      <td>0.5367%</td>\n",
       "      <td>-0.4607%</td>\n",
       "      <td>0.5800%</td>\n",
       "      <td>-1.9919%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 APA       BA      BAX      BMY    CMCSA      CNP      CPB  \\\n",
       "Date                                                                         \n",
       "2016-01-05  -2.0257%  0.4057%  0.4036%  1.9693%  0.0180%  0.9305%  0.3678%   \n",
       "2016-01-06 -11.4863% -1.5879%  0.2412% -1.7557% -0.7727% -1.2473% -0.1736%   \n",
       "2016-01-07  -5.1389% -4.1922% -1.6573% -2.7699% -1.1047% -1.9769% -1.2206%   \n",
       "2016-01-08   0.2736% -2.2705% -1.6037% -2.5425%  0.1099% -0.2241%  0.5706%   \n",
       "2016-01-11  -4.3383%  0.1693% -1.6851% -1.0215%  0.0915% -1.1791%  0.5674%   \n",
       "\n",
       "                 DE      HPQ      JCI  ...       NI     PCAR      PSA  \\\n",
       "Date                                   ...                              \n",
       "2016-01-05  0.5783%  0.9483% -1.1953%  ...  1.5881%  0.0212%  2.8236%   \n",
       "2016-01-06 -1.1238% -3.5867% -0.9551%  ...  0.5547%  0.0212%  0.1592%   \n",
       "2016-01-07 -0.8856% -4.6058% -2.5394%  ... -2.2067% -3.0310% -1.0410%   \n",
       "2016-01-08 -1.6402% -1.7642% -0.1649%  ... -0.1538% -1.1366% -0.7308%   \n",
       "2016-01-11  0.5288%  0.6616%  0.0330%  ...  1.6436%  0.0000%  0.9869%   \n",
       "\n",
       "                SEE        T      TGT      TMO      TXT       VZ     ZION  \n",
       "Date                                                                       \n",
       "2016-01-05  0.9758%  0.6987%  1.7539% -0.1730%  0.2409%  1.3735% -1.0857%  \n",
       "2016-01-06 -1.5646%  0.3108% -1.0155% -0.7652% -3.0048% -0.9035% -2.9145%  \n",
       "2016-01-07 -3.1557% -1.6148% -0.2700% -2.2845% -2.0570% -0.5492% -3.0019%  \n",
       "2016-01-08 -0.1449%  0.0895% -3.3839% -0.1117% -1.1387% -0.9720% -1.1254%  \n",
       "2016-01-11 -0.1450%  1.2224%  1.4570%  0.5367% -0.4607%  0.5800% -1.9919%  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating returns\n",
    "\n",
    "Y = data[assets].pct_change().dropna()\n",
    "\n",
    "display(Y.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import riskfolio as rp\n",
    "\n",
    "# Plotting Assets Clusters\n",
    "\n",
    "ax = rp.plot_clusters(returns=Y,\n",
    "                      codependence='pearson',\n",
    "                      linkage='ward',\n",
    "                      k=None,\n",
    "                      max_k=10,\n",
    "                      leaf_order=True,\n",
    "                      dendrogram=True,\n",
    "                      #linecolor='tab:purple',\n",
    "                      ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph above suggest that optimal number of clusters are four.\n",
    "\n",
    "## 2. Estimating NCO Portfolio\n",
    "\n",
    "This is the original model proposed by López de Prado (2019). Riskfolio-Lib expand this model to 13 risk measures and for objective functions: \"Minimize Risk\", \"Maximize Utility Function\", \"Maximize Return/Risk Ratio\" and \"Equal Risk Contribution\".\n",
    "\n",
    "### 2.1 Calculating the NCO portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.1729%</td>\n",
       "      <td>5.1828%</td>\n",
       "      <td>2.8237%</td>\n",
       "      <td>5.0010%</td>\n",
       "      <td>10.9745%</td>\n",
       "      <td>3.7074%</td>\n",
       "      <td>0.7664%</td>\n",
       "      <td>0.3470%</td>\n",
       "      <td>2.4532%</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0044%</td>\n",
       "      <td>0.1994%</td>\n",
       "      <td>12.0257%</td>\n",
       "      <td>2.2921%</td>\n",
       "      <td>4.8852%</td>\n",
       "      <td>3.0259%</td>\n",
       "      <td>1.1104%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>8.6247%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            APA      BA     BAX     BMY   CMCSA      CNP     CPB      DE  \\\n",
       "weights 0.0000% 1.1729% 5.1828% 2.8237% 5.0010% 10.9745% 3.7074% 0.7664%   \n",
       "\n",
       "            HPQ     JCI  ...      NI    PCAR      PSA     SEE       T     TGT  \\\n",
       "weights 0.3470% 2.4532%  ... 7.0044% 0.1994% 12.0257% 2.2921% 4.8852% 3.0259%   \n",
       "\n",
       "            TMO     TXT      VZ    ZION  \n",
       "weights 1.1104% 0.0000% 8.6247% 0.0000%  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Building the portfolio object\n",
    "port = rp.HCPortfolio(returns=Y)\n",
    "\n",
    "# Estimate optimal portfolio:\n",
    "\n",
    "model='NCO' # Could be HRP, HERC or NCO\n",
    "codependence = 'pearson' # Correlation matrix used to group assets in clusters\n",
    "method_cov = 'hist' # Covariance estimation technique\n",
    "obj = \"MinRisk\" # Posible values are \"MinRisk\", \"Utility\", \"Sharpe\" and \"ERC\"\n",
    "rm = 'MV' # Risk measure used, this time will be variance\n",
    "rf = 0 # Risk free rate\n",
    "l = 2 # Risk aversion factor, only usefull with \"Utility\" objective\n",
    "linkage = 'ward' # Linkage method used to build clusters\n",
    "max_k = 10 # Max number of clusters used in two difference gap statistic\n",
    "leaf_order = True # Consider optimal order of leafs in dendrogram\n",
    "\n",
    "w = port.optimization(model=model,\n",
    "                      codependence=codependence,\n",
    "                      method_cov=method_cov,\n",
    "                      obj=obj,\n",
    "                      rm=rm,\n",
    "                      rf=rf,\n",
    "                      l=l,\n",
    "                      linkage=linkage,\n",
    "                      max_k=max_k,\n",
    "                      leaf_order=leaf_order)\n",
    "\n",
    "display(w.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Plotting portfolio composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the composition of the portfolio\n",
    "\n",
    "ax = rp.plot_pie(w=w,\n",
    "                 title='NCO MinRisk',\n",
    "                 others=0.05,\n",
    "                 nrow=25,\n",
    "                 cmap=\"tab20\",\n",
    "                 height=8,\n",
    "                 width=10,\n",
    "                 ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Plotting Risk Contribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the risk contribution per asset\n",
    "\n",
    "mu = Y.mean()\n",
    "cov = Y.cov() # Covariance matrix\n",
    "returns = Y # Returns of the assets\n",
    "\n",
    "ax = rp.plot_risk_con(w=w,\n",
    "                      cov=cov,\n",
    "                      returns=returns,\n",
    "                      rm=rm,\n",
    "                      rf=0,\n",
    "                      alpha=0.05,\n",
    "                      color=\"tab:blue\",\n",
    "                      height=6,\n",
    "                      width=10,\n",
    "                      t_factor=252,\n",
    "                      ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Calculate Optimal NCO Portfolios for Several Risk Measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Risk Measures available:\n",
    "#\n",
    "# 'MV': Variance.\n",
    "# 'MAD': Mean Absolute Deviation.\n",
    "# 'MSV': Semi Standard Deviation.\n",
    "# 'FLPM': First Lower Partial Moment (Omega Ratio).\n",
    "# 'SLPM': Second Lower Partial Moment (Sortino Ratio).\n",
    "# 'CVaR': Conditional Value at Risk.\n",
    "# 'EVaR': Entropic Value at Risk.\n",
    "# 'WR': Worst Realization (Minimax)\n",
    "# 'MDD': Maximum Drawdown of uncompounded cumulative returns (Calmar Ratio).\n",
    "# 'ADD': Average Drawdown of uncompounded cumulative returns.\n",
    "# 'CDaR': Conditional Drawdown at Risk of uncompounded cumulative returns.\n",
    "# 'EDaR': Entropic Drawdown at Risk of uncompounded cumulative returns.\n",
    "# 'UCI': Ulcer Index of uncompounded cumulative returns.\n",
    "\n",
    "rms = ['MV', 'MAD', 'MSV', 'FLPM', 'SLPM',\n",
    "       'CVaR', 'EVaR', 'WR', 'MDD', 'ADD',\n",
    "       'CDaR', 'EDaR', 'UCI']\n",
    "\n",
    "w_s = pd.DataFrame([])\n",
    "\n",
    "for i in rms:\n",
    "    w = port.optimization(model=model,\n",
    "                          codependence=codependence,\n",
    "                          method_cov=method_cov,\n",
    "                          obj=obj,\n",
    "                          rm=i,\n",
    "                          rf=rf,\n",
    "                          linkage=linkage,\n",
    "                          max_k=max_k,\n",
    "                          leaf_order=leaf_order)\n",
    "\n",
    "    w_s = pd.concat([w_s, w], axis=1)\n",
    "    \n",
    "w_s.columns = rms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "  color: #000000;\n",
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       "#T_d5ff4_row2_col1 {\n",
       "  background-color: #c3e698;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row2_col2, #T_d5ff4_row2_col10, #T_d5ff4_row6_col4 {\n",
       "  background-color: #c0e597;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row2_col3, #T_d5ff4_row21_col12 {\n",
       "  background-color: #bce395;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row2_col4, #T_d5ff4_row2_col6, #T_d5ff4_row2_col12 {\n",
       "  background-color: #bee596;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row2_col5 {\n",
       "  background-color: #c5e89a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row2_col9, #T_d5ff4_row15_col7 {\n",
       "  background-color: #84cb7e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row2_col11, #T_d5ff4_row6_col3, #T_d5ff4_row9_col2, #T_d5ff4_row14_col4, #T_d5ff4_row19_col9 {\n",
       "  background-color: #f6fcb8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row3_col0, #T_d5ff4_row19_col3, #T_d5ff4_row19_col4 {\n",
       "  background-color: #e1f3a9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row3_col1 {\n",
       "  background-color: #e2f4aa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row3_col2 {\n",
       "  background-color: #ecf7b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row3_col3, #T_d5ff4_row14_col3 {\n",
       "  background-color: #f0f9b4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row3_col4 {\n",
       "  background-color: #f1fab5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row3_col5, #T_d5ff4_row18_col2, #T_d5ff4_row21_col6 {\n",
       "  background-color: #f8fcbd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row3_col6, #T_d5ff4_row10_col11, #T_d5ff4_row14_col11, #T_d5ff4_row20_col2, #T_d5ff4_row21_col10 {\n",
       "  background-color: #daf0a4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row3_col9, #T_d5ff4_row4_col8, #T_d5ff4_row7_col9, #T_d5ff4_row8_col0, #T_d5ff4_row10_col5, #T_d5ff4_row15_col11, #T_d5ff4_row16_col2, #T_d5ff4_row20_col6 {\n",
       "  background-color: #fdfedd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row3_col11, #T_d5ff4_row7_col4, #T_d5ff4_row8_col1, #T_d5ff4_row8_col3, #T_d5ff4_row18_col9, #T_d5ff4_row19_col6, #T_d5ff4_row20_col8 {\n",
       "  background-color: #fdfed9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row3_col12, #T_d5ff4_row9_col5, #T_d5ff4_row18_col5 {\n",
       "  background-color: #feffdf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row4_col0 {\n",
       "  background-color: #a9db8c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row4_col1 {\n",
       "  background-color: #bae394;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row4_col2 {\n",
       "  background-color: #c4e799;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row4_col3, #T_d5ff4_row4_col4 {\n",
       "  background-color: #c9e99c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row4_col5, #T_d5ff4_row9_col0 {\n",
       "  background-color: #e8f6ae;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row4_col6, #T_d5ff4_row16_col1, #T_d5ff4_row16_col4 {\n",
       "  background-color: #feffde;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row4_col12, #T_d5ff4_row7_col5, #T_d5ff4_row19_col12, #T_d5ff4_row23_col12 {\n",
       "  background-color: #ffffe4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row5_col0, #T_d5ff4_row17_col11 {\n",
       "  background-color: #086e3a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row5_col1 {\n",
       "  background-color: #45ad5f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row5_col2 {\n",
       "  background-color: #15793e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row5_col3 {\n",
       "  background-color: #64bc6f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row5_col4 {\n",
       "  background-color: #238443;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row5_col5, #T_d5ff4_row5_col8, #T_d5ff4_row5_col11, #T_d5ff4_row12_col0, #T_d5ff4_row12_col1, #T_d5ff4_row12_col2, #T_d5ff4_row12_col3, #T_d5ff4_row12_col4, #T_d5ff4_row12_col6, #T_d5ff4_row12_col9, #T_d5ff4_row12_col10, #T_d5ff4_row12_col12, #T_d5ff4_row17_col7 {\n",
       "  background-color: #004529;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row5_col6 {\n",
       "  background-color: #68be71;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row5_col9, #T_d5ff4_row6_col5 {\n",
       "  background-color: #9ad587;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row5_col10, #T_d5ff4_row14_col10 {\n",
       "  background-color: #004629;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row5_col12, #T_d5ff4_row13_col11 {\n",
       "  background-color: #349a52;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row6_col0, #T_d5ff4_row10_col1 {\n",
       "  background-color: #ccea9d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row6_col1 {\n",
       "  background-color: #e3f4aa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row6_col2, #T_d5ff4_row15_col5 {\n",
       "  background-color: #aedd8e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row6_col6 {\n",
       "  background-color: #d9f0a3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row6_col7, #T_d5ff4_row7_col0, #T_d5ff4_row7_col10 {\n",
       "  background-color: #fbfed0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row7_col1, #T_d5ff4_row18_col11 {\n",
       "  background-color: #fbfdce;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row7_col3, #T_d5ff4_row11_col4, #T_d5ff4_row18_col8, #T_d5ff4_row19_col8 {\n",
       "  background-color: #fbfed2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row7_col8, #T_d5ff4_row13_col3 {\n",
       "  background-color: #93d284;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row7_col11 {\n",
       "  background-color: #c8e99b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row9_col1 {\n",
       "  background-color: #eaf7af;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row9_col3 {\n",
       "  background-color: #f7fcba;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row9_col9, #T_d5ff4_row23_col8 {\n",
       "  background-color: #fbfdcf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row9_col10, #T_d5ff4_row11_col5, #T_d5ff4_row16_col0 {\n",
       "  background-color: #feffe1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row10_col0 {\n",
       "  background-color: #e6f5ac;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row10_col2, #T_d5ff4_row18_col1, #T_d5ff4_row20_col1, #T_d5ff4_row20_col3 {\n",
       "  background-color: #edf8b2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row10_col3 {\n",
       "  background-color: #d6efa2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row10_col4, #T_d5ff4_row14_col5, #T_d5ff4_row23_col9 {\n",
       "  background-color: #f3fab6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row10_col9 {\n",
       "  background-color: #f8fcc0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row10_col10, #T_d5ff4_row11_col0, #T_d5ff4_row14_col2 {\n",
       "  background-color: #f9fdc2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row11_col1 {\n",
       "  background-color: #f7fcb9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row11_col2, #T_d5ff4_row18_col4, #T_d5ff4_row23_col6 {\n",
       "  background-color: #fafdc9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row11_col3 {\n",
       "  background-color: #fafdcb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row12_col5 {\n",
       "  background-color: #12763d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row12_col11 {\n",
       "  background-color: #95d385;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row13_col0, #T_d5ff4_row17_col6 {\n",
       "  background-color: #14783e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row13_col1 {\n",
       "  background-color: #4cb063;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row13_col2, #T_d5ff4_row13_col5 {\n",
       "  background-color: #3ea75a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row13_col4, #T_d5ff4_row15_col0 {\n",
       "  background-color: #66bd70;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row13_col6 {\n",
       "  background-color: #69bf72;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row13_col7 {\n",
       "  background-color: #4ab062;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row13_col8 {\n",
       "  background-color: #5fba6c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row13_col10 {\n",
       "  background-color: #359c53;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row13_col12 {\n",
       "  background-color: #c7e89a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row14_col1 {\n",
       "  background-color: #f5fbb8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row14_col9 {\n",
       "  background-color: #00502d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row14_col12 {\n",
       "  background-color: #006737;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row15_col1 {\n",
       "  background-color: #83cb7d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row15_col2, #T_d5ff4_row23_col3 {\n",
       "  background-color: #a6da8b;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row15_col3 {\n",
       "  background-color: #b2df90;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row15_col4 {\n",
       "  background-color: #b3e091;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row15_col10, #T_d5ff4_row16_col3 {\n",
       "  background-color: #fdfeda;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row17_col0 {\n",
       "  background-color: #005931;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row17_col1 {\n",
       "  background-color: #3ba358;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row17_col2 {\n",
       "  background-color: #005d33;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row17_col3 {\n",
       "  background-color: #7fc97c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row17_col4 {\n",
       "  background-color: #1e8041;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row17_col5 {\n",
       "  background-color: #006636;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row17_col8 {\n",
       "  background-color: #005a31;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row17_col9, #T_d5ff4_row19_col2 {\n",
       "  background-color: #dcf1a5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row17_col10 {\n",
       "  background-color: #0b713b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row17_col12 {\n",
       "  background-color: #a1d889;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row18_col0 {\n",
       "  background-color: #ebf7b0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row18_col10, #T_d5ff4_row22_col1 {\n",
       "  background-color: #fdfedb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row19_col0 {\n",
       "  background-color: #acdd8e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row19_col1 {\n",
       "  background-color: #d3eda0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row19_col5 {\n",
       "  background-color: #d5eea1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row20_col0 {\n",
       "  background-color: #ddf2a6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row20_col4 {\n",
       "  background-color: #dbf1a4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row20_col5 {\n",
       "  background-color: #bde496;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row21_col0 {\n",
       "  background-color: #fafdc8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row21_col9 {\n",
       "  background-color: #ceeb9e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row23_col0 {\n",
       "  background-color: #379e54;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row23_col1 {\n",
       "  background-color: #86cc7f;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d5ff4_row23_col2 {\n",
       "  background-color: #40aa5c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row23_col4 {\n",
       "  background-color: #4fb264;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d5ff4_row23_col5 {\n",
       "  background-color: #096f3a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_d5ff4\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d5ff4_level0_col0\" class=\"col_heading level0 col0\" >MV</th>\n",
       "      <th id=\"T_d5ff4_level0_col1\" class=\"col_heading level0 col1\" >MAD</th>\n",
       "      <th id=\"T_d5ff4_level0_col2\" class=\"col_heading level0 col2\" >MSV</th>\n",
       "      <th id=\"T_d5ff4_level0_col3\" class=\"col_heading level0 col3\" >FLPM</th>\n",
       "      <th id=\"T_d5ff4_level0_col4\" class=\"col_heading level0 col4\" >SLPM</th>\n",
       "      <th id=\"T_d5ff4_level0_col5\" class=\"col_heading level0 col5\" >CVaR</th>\n",
       "      <th id=\"T_d5ff4_level0_col6\" class=\"col_heading level0 col6\" >EVaR</th>\n",
       "      <th id=\"T_d5ff4_level0_col7\" class=\"col_heading level0 col7\" >WR</th>\n",
       "      <th id=\"T_d5ff4_level0_col8\" class=\"col_heading level0 col8\" >MDD</th>\n",
       "      <th id=\"T_d5ff4_level0_col9\" class=\"col_heading level0 col9\" >ADD</th>\n",
       "      <th id=\"T_d5ff4_level0_col10\" class=\"col_heading level0 col10\" >CDaR</th>\n",
       "      <th id=\"T_d5ff4_level0_col11\" class=\"col_heading level0 col11\" >EDaR</th>\n",
       "      <th id=\"T_d5ff4_level0_col12\" class=\"col_heading level0 col12\" >UCI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row0\" class=\"row_heading level0 row0\" >APA</th>\n",
       "      <td id=\"T_d5ff4_row0_col0\" class=\"data row0 col0\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row0_col1\" class=\"data row0 col1\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row0_col2\" class=\"data row0 col2\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row0_col3\" class=\"data row0 col3\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row0_col4\" class=\"data row0 col4\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row0_col5\" class=\"data row0 col5\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row0_col6\" class=\"data row0 col6\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row0_col7\" class=\"data row0 col7\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row0_col8\" class=\"data row0 col8\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row0_col9\" class=\"data row0 col9\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row0_col10\" class=\"data row0 col10\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row0_col11\" class=\"data row0 col11\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row0_col12\" class=\"data row0 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row1\" class=\"row_heading level0 row1\" >BA</th>\n",
       "      <td id=\"T_d5ff4_row1_col0\" class=\"data row1 col0\" >1.17%</td>\n",
       "      <td id=\"T_d5ff4_row1_col1\" class=\"data row1 col1\" >1.61%</td>\n",
       "      <td id=\"T_d5ff4_row1_col2\" class=\"data row1 col2\" >0.67%</td>\n",
       "      <td id=\"T_d5ff4_row1_col3\" class=\"data row1 col3\" >1.58%</td>\n",
       "      <td id=\"T_d5ff4_row1_col4\" class=\"data row1 col4\" >0.64%</td>\n",
       "      <td id=\"T_d5ff4_row1_col5\" class=\"data row1 col5\" >0.14%</td>\n",
       "      <td id=\"T_d5ff4_row1_col6\" class=\"data row1 col6\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row1_col7\" class=\"data row1 col7\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row1_col8\" class=\"data row1 col8\" >4.28%</td>\n",
       "      <td id=\"T_d5ff4_row1_col9\" class=\"data row1 col9\" >2.17%</td>\n",
       "      <td id=\"T_d5ff4_row1_col10\" class=\"data row1 col10\" >0.86%</td>\n",
       "      <td id=\"T_d5ff4_row1_col11\" class=\"data row1 col11\" >2.72%</td>\n",
       "      <td id=\"T_d5ff4_row1_col12\" class=\"data row1 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row2\" class=\"row_heading level0 row2\" >BAX</th>\n",
       "      <td id=\"T_d5ff4_row2_col0\" class=\"data row2 col0\" >5.18%</td>\n",
       "      <td id=\"T_d5ff4_row2_col1\" class=\"data row2 col1\" >4.84%</td>\n",
       "      <td id=\"T_d5ff4_row2_col2\" class=\"data row2 col2\" >4.73%</td>\n",
       "      <td id=\"T_d5ff4_row2_col3\" class=\"data row2 col3\" >6.09%</td>\n",
       "      <td id=\"T_d5ff4_row2_col4\" class=\"data row2 col4\" >5.19%</td>\n",
       "      <td id=\"T_d5ff4_row2_col5\" class=\"data row2 col5\" >4.79%</td>\n",
       "      <td id=\"T_d5ff4_row2_col6\" class=\"data row2 col6\" >8.18%</td>\n",
       "      <td id=\"T_d5ff4_row2_col7\" class=\"data row2 col7\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row2_col8\" class=\"data row2 col8\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row2_col9\" class=\"data row2 col9\" >10.86%</td>\n",
       "      <td id=\"T_d5ff4_row2_col10\" class=\"data row2 col10\" >6.00%</td>\n",
       "      <td id=\"T_d5ff4_row2_col11\" class=\"data row2 col11\" >3.13%</td>\n",
       "      <td id=\"T_d5ff4_row2_col12\" class=\"data row2 col12\" >8.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row3\" class=\"row_heading level0 row3\" >BMY</th>\n",
       "      <td id=\"T_d5ff4_row3_col0\" class=\"data row3 col0\" >2.82%</td>\n",
       "      <td id=\"T_d5ff4_row3_col1\" class=\"data row3 col1\" >3.26%</td>\n",
       "      <td id=\"T_d5ff4_row3_col2\" class=\"data row3 col2\" >2.52%</td>\n",
       "      <td id=\"T_d5ff4_row3_col3\" class=\"data row3 col3\" >2.79%</td>\n",
       "      <td id=\"T_d5ff4_row3_col4\" class=\"data row3 col4\" >2.38%</td>\n",
       "      <td id=\"T_d5ff4_row3_col5\" class=\"data row3 col5\" >1.81%</td>\n",
       "      <td id=\"T_d5ff4_row3_col6\" class=\"data row3 col6\" >6.25%</td>\n",
       "      <td id=\"T_d5ff4_row3_col7\" class=\"data row3 col7\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row3_col8\" class=\"data row3 col8\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row3_col9\" class=\"data row3 col9\" >0.60%</td>\n",
       "      <td id=\"T_d5ff4_row3_col10\" class=\"data row3 col10\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row3_col11\" class=\"data row3 col11\" >0.87%</td>\n",
       "      <td id=\"T_d5ff4_row3_col12\" class=\"data row3 col12\" >0.41%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row4\" class=\"row_heading level0 row4\" >CMCSA</th>\n",
       "      <td id=\"T_d5ff4_row4_col0\" class=\"data row4 col0\" >5.00%</td>\n",
       "      <td id=\"T_d5ff4_row4_col1\" class=\"data row4 col1\" >5.21%</td>\n",
       "      <td id=\"T_d5ff4_row4_col2\" class=\"data row4 col2\" >4.54%</td>\n",
       "      <td id=\"T_d5ff4_row4_col3\" class=\"data row4 col3\" >5.40%</td>\n",
       "      <td id=\"T_d5ff4_row4_col4\" class=\"data row4 col4\" >4.72%</td>\n",
       "      <td id=\"T_d5ff4_row4_col5\" class=\"data row4 col5\" >2.94%</td>\n",
       "      <td id=\"T_d5ff4_row4_col6\" class=\"data row4 col6\" >0.49%</td>\n",
       "      <td id=\"T_d5ff4_row4_col7\" class=\"data row4 col7\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row4_col8\" class=\"data row4 col8\" >0.71%</td>\n",
       "      <td id=\"T_d5ff4_row4_col9\" class=\"data row4 col9\" >3.36%</td>\n",
       "      <td id=\"T_d5ff4_row4_col10\" class=\"data row4 col10\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row4_col11\" class=\"data row4 col11\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row4_col12\" class=\"data row4 col12\" >0.11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row5\" class=\"row_heading level0 row5\" >CNP</th>\n",
       "      <td id=\"T_d5ff4_row5_col0\" class=\"data row5 col0\" >10.97%</td>\n",
       "      <td id=\"T_d5ff4_row5_col1\" class=\"data row5 col1\" >9.44%</td>\n",
       "      <td id=\"T_d5ff4_row5_col2\" class=\"data row5 col2\" >11.69%</td>\n",
       "      <td id=\"T_d5ff4_row5_col3\" class=\"data row5 col3\" >9.96%</td>\n",
       "      <td id=\"T_d5ff4_row5_col4\" class=\"data row5 col4\" >11.93%</td>\n",
       "      <td id=\"T_d5ff4_row5_col5\" class=\"data row5 col5\" >15.62%</td>\n",
       "      <td id=\"T_d5ff4_row5_col6\" class=\"data row5 col6\" >13.49%</td>\n",
       "      <td id=\"T_d5ff4_row5_col7\" class=\"data row5 col7\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row5_col8\" class=\"data row5 col8\" >29.30%</td>\n",
       "      <td id=\"T_d5ff4_row5_col9\" class=\"data row5 col9\" >9.69%</td>\n",
       "      <td id=\"T_d5ff4_row5_col10\" class=\"data row5 col10\" >18.59%</td>\n",
       "      <td id=\"T_d5ff4_row5_col11\" class=\"data row5 col11\" >24.27%</td>\n",
       "      <td id=\"T_d5ff4_row5_col12\" class=\"data row5 col12\" >17.15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row6\" class=\"row_heading level0 row6\" >CPB</th>\n",
       "      <td id=\"T_d5ff4_row6_col0\" class=\"data row6 col0\" >3.71%</td>\n",
       "      <td id=\"T_d5ff4_row6_col1\" class=\"data row6 col1\" >3.20%</td>\n",
       "      <td id=\"T_d5ff4_row6_col2\" class=\"data row6 col2\" >5.47%</td>\n",
       "      <td id=\"T_d5ff4_row6_col3\" class=\"data row6 col3\" >2.39%</td>\n",
       "      <td id=\"T_d5ff4_row6_col4\" class=\"data row6 col4\" >5.16%</td>\n",
       "      <td id=\"T_d5ff4_row6_col5\" class=\"data row6 col5\" >6.55%</td>\n",
       "      <td id=\"T_d5ff4_row6_col6\" class=\"data row6 col6\" >6.27%</td>\n",
       "      <td id=\"T_d5ff4_row6_col7\" class=\"data row6 col7\" >2.83%</td>\n",
       "      <td id=\"T_d5ff4_row6_col8\" class=\"data row6 col8\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row6_col9\" class=\"data row6 col9\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row6_col10\" class=\"data row6 col10\" >0.03%</td>\n",
       "      <td id=\"T_d5ff4_row6_col11\" class=\"data row6 col11\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row6_col12\" class=\"data row6 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row7\" class=\"row_heading level0 row7\" >DE</th>\n",
       "      <td id=\"T_d5ff4_row7_col0\" class=\"data row7 col0\" >0.77%</td>\n",
       "      <td id=\"T_d5ff4_row7_col1\" class=\"data row7 col1\" >1.02%</td>\n",
       "      <td id=\"T_d5ff4_row7_col2\" class=\"data row7 col2\" >0.59%</td>\n",
       "      <td id=\"T_d5ff4_row7_col3\" class=\"data row7 col3\" >1.03%</td>\n",
       "      <td id=\"T_d5ff4_row7_col4\" class=\"data row7 col4\" >0.61%</td>\n",
       "      <td id=\"T_d5ff4_row7_col5\" class=\"data row7 col5\" >0.10%</td>\n",
       "      <td id=\"T_d5ff4_row7_col6\" class=\"data row7 col6\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row7_col7\" class=\"data row7 col7\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row7_col8\" class=\"data row7 col8\" >12.76%</td>\n",
       "      <td id=\"T_d5ff4_row7_col9\" class=\"data row7 col9\" >0.62%</td>\n",
       "      <td id=\"T_d5ff4_row7_col10\" class=\"data row7 col10\" >1.16%</td>\n",
       "      <td id=\"T_d5ff4_row7_col11\" class=\"data row7 col11\" >7.23%</td>\n",
       "      <td id=\"T_d5ff4_row7_col12\" class=\"data row7 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row8\" class=\"row_heading level0 row8\" >HPQ</th>\n",
       "      <td id=\"T_d5ff4_row8_col0\" class=\"data row8 col0\" >0.35%</td>\n",
       "      <td id=\"T_d5ff4_row8_col1\" class=\"data row8 col1\" >0.58%</td>\n",
       "      <td id=\"T_d5ff4_row8_col2\" class=\"data row8 col2\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row8_col3\" class=\"data row8 col3\" >0.68%</td>\n",
       "      <td id=\"T_d5ff4_row8_col4\" class=\"data row8 col4\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row8_col5\" class=\"data row8 col5\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row8_col6\" class=\"data row8 col6\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row8_col7\" class=\"data row8 col7\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row8_col8\" class=\"data row8 col8\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row8_col9\" class=\"data row8 col9\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row8_col10\" class=\"data row8 col10\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row8_col11\" class=\"data row8 col11\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row8_col12\" class=\"data row8 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row9\" class=\"row_heading level0 row9\" >JCI</th>\n",
       "      <td id=\"T_d5ff4_row9_col0\" class=\"data row9 col0\" >2.45%</td>\n",
       "      <td id=\"T_d5ff4_row9_col1\" class=\"data row9 col1\" >2.77%</td>\n",
       "      <td id=\"T_d5ff4_row9_col2\" class=\"data row9 col2\" >1.89%</td>\n",
       "      <td id=\"T_d5ff4_row9_col3\" class=\"data row9 col3\" >2.26%</td>\n",
       "      <td id=\"T_d5ff4_row9_col4\" class=\"data row9 col4\" >1.54%</td>\n",
       "      <td id=\"T_d5ff4_row9_col5\" class=\"data row9 col5\" >0.28%</td>\n",
       "      <td id=\"T_d5ff4_row9_col6\" class=\"data row9 col6\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row9_col7\" class=\"data row9 col7\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row9_col8\" class=\"data row9 col8\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row9_col9\" class=\"data row9 col9\" >1.45%</td>\n",
       "      <td id=\"T_d5ff4_row9_col10\" class=\"data row9 col10\" >0.23%</td>\n",
       "      <td id=\"T_d5ff4_row9_col11\" class=\"data row9 col11\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row9_col12\" class=\"data row9 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row10\" class=\"row_heading level0 row10\" >JPM</th>\n",
       "      <td id=\"T_d5ff4_row10_col0\" class=\"data row10 col0\" >2.57%</td>\n",
       "      <td id=\"T_d5ff4_row10_col1\" class=\"data row10 col1\" >4.42%</td>\n",
       "      <td id=\"T_d5ff4_row10_col2\" class=\"data row10 col2\" >2.44%</td>\n",
       "      <td id=\"T_d5ff4_row10_col3\" class=\"data row10 col3\" >4.77%</td>\n",
       "      <td id=\"T_d5ff4_row10_col4\" class=\"data row10 col4\" >2.27%</td>\n",
       "      <td id=\"T_d5ff4_row10_col5\" class=\"data row10 col5\" >0.39%</td>\n",
       "      <td id=\"T_d5ff4_row10_col6\" class=\"data row10 col6\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row10_col7\" class=\"data row10 col7\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row10_col8\" class=\"data row10 col8\" >3.25%</td>\n",
       "      <td id=\"T_d5ff4_row10_col9\" class=\"data row10 col9\" >2.52%</td>\n",
       "      <td id=\"T_d5ff4_row10_col10\" class=\"data row10 col10\" >1.90%</td>\n",
       "      <td id=\"T_d5ff4_row10_col11\" class=\"data row10 col11\" >6.00%</td>\n",
       "      <td id=\"T_d5ff4_row10_col12\" class=\"data row10 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row11\" class=\"row_heading level0 row11\" >LUV</th>\n",
       "      <td id=\"T_d5ff4_row11_col0\" class=\"data row11 col0\" >1.28%</td>\n",
       "      <td id=\"T_d5ff4_row11_col1\" class=\"data row11 col1\" >1.94%</td>\n",
       "      <td id=\"T_d5ff4_row11_col2\" class=\"data row11 col2\" >1.16%</td>\n",
       "      <td id=\"T_d5ff4_row11_col3\" class=\"data row11 col3\" >1.42%</td>\n",
       "      <td id=\"T_d5ff4_row11_col4\" class=\"data row11 col4\" >0.91%</td>\n",
       "      <td id=\"T_d5ff4_row11_col5\" class=\"data row11 col5\" >0.21%</td>\n",
       "      <td id=\"T_d5ff4_row11_col6\" class=\"data row11 col6\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row11_col7\" class=\"data row11 col7\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row11_col8\" class=\"data row11 col8\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row11_col9\" class=\"data row11 col9\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row11_col10\" class=\"data row11 col10\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row11_col11\" class=\"data row11 col11\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row11_col12\" class=\"data row11 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row12\" class=\"row_heading level0 row12\" >MMC</th>\n",
       "      <td id=\"T_d5ff4_row12_col0\" class=\"data row12 col0\" >12.96%</td>\n",
       "      <td id=\"T_d5ff4_row12_col1\" class=\"data row12 col1\" >15.34%</td>\n",
       "      <td id=\"T_d5ff4_row12_col2\" class=\"data row12 col2\" >14.65%</td>\n",
       "      <td id=\"T_d5ff4_row12_col3\" class=\"data row12 col3\" >18.23%</td>\n",
       "      <td id=\"T_d5ff4_row12_col4\" class=\"data row12 col4\" >15.97%</td>\n",
       "      <td id=\"T_d5ff4_row12_col5\" class=\"data row12 col5\" >12.68%</td>\n",
       "      <td id=\"T_d5ff4_row12_col6\" class=\"data row12 col6\" >25.05%</td>\n",
       "      <td id=\"T_d5ff4_row12_col7\" class=\"data row12 col7\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row12_col8\" class=\"data row12 col8\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row12_col9\" class=\"data row12 col9\" >23.07%</td>\n",
       "      <td id=\"T_d5ff4_row12_col10\" class=\"data row12 col10\" >18.69%</td>\n",
       "      <td id=\"T_d5ff4_row12_col11\" class=\"data row12 col11\" >10.43%</td>\n",
       "      <td id=\"T_d5ff4_row12_col12\" class=\"data row12 col12\" >25.28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row13\" class=\"row_heading level0 row13\" >MO</th>\n",
       "      <td id=\"T_d5ff4_row13_col0\" class=\"data row13 col0\" >10.42%</td>\n",
       "      <td id=\"T_d5ff4_row13_col1\" class=\"data row13 col1\" >9.21%</td>\n",
       "      <td id=\"T_d5ff4_row13_col2\" class=\"data row13 col2\" >9.34%</td>\n",
       "      <td id=\"T_d5ff4_row13_col3\" class=\"data row13 col3\" >7.93%</td>\n",
       "      <td id=\"T_d5ff4_row13_col4\" class=\"data row13 col4\" >8.62%</td>\n",
       "      <td id=\"T_d5ff4_row13_col5\" class=\"data row13 col5\" >9.96%</td>\n",
       "      <td id=\"T_d5ff4_row13_col6\" class=\"data row13 col6\" >13.38%</td>\n",
       "      <td id=\"T_d5ff4_row13_col7\" class=\"data row13 col7\" >28.30%</td>\n",
       "      <td id=\"T_d5ff4_row13_col8\" class=\"data row13 col8\" >16.30%</td>\n",
       "      <td id=\"T_d5ff4_row13_col9\" class=\"data row13 col9\" >3.35%</td>\n",
       "      <td id=\"T_d5ff4_row13_col10\" class=\"data row13 col10\" >12.61%</td>\n",
       "      <td id=\"T_d5ff4_row13_col11\" class=\"data row13 col11\" >16.44%</td>\n",
       "      <td id=\"T_d5ff4_row13_col12\" class=\"data row13 col12\" >7.61%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row14\" class=\"row_heading level0 row14\" >MSFT</th>\n",
       "      <td id=\"T_d5ff4_row14_col0\" class=\"data row14 col0\" >1.17%</td>\n",
       "      <td id=\"T_d5ff4_row14_col1\" class=\"data row14 col1\" >2.07%</td>\n",
       "      <td id=\"T_d5ff4_row14_col2\" class=\"data row14 col2\" >1.47%</td>\n",
       "      <td id=\"T_d5ff4_row14_col3\" class=\"data row14 col3\" >2.84%</td>\n",
       "      <td id=\"T_d5ff4_row14_col4\" class=\"data row14 col4\" >2.06%</td>\n",
       "      <td id=\"T_d5ff4_row14_col5\" class=\"data row14 col5\" >2.25%</td>\n",
       "      <td id=\"T_d5ff4_row14_col6\" class=\"data row14 col6\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row14_col7\" class=\"data row14 col7\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row14_col8\" class=\"data row14 col8\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row14_col9\" class=\"data row14 col9\" >22.11%</td>\n",
       "      <td id=\"T_d5ff4_row14_col10\" class=\"data row14 col10\" >18.57%</td>\n",
       "      <td id=\"T_d5ff4_row14_col11\" class=\"data row14 col11\" >6.04%</td>\n",
       "      <td id=\"T_d5ff4_row14_col12\" class=\"data row14 col12\" >22.19%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row15\" class=\"row_heading level0 row15\" >NI</th>\n",
       "      <td id=\"T_d5ff4_row15_col0\" class=\"data row15 col0\" >7.00%</td>\n",
       "      <td id=\"T_d5ff4_row15_col1\" class=\"data row15 col1\" >7.29%</td>\n",
       "      <td id=\"T_d5ff4_row15_col2\" class=\"data row15 col2\" >5.73%</td>\n",
       "      <td id=\"T_d5ff4_row15_col3\" class=\"data row15 col3\" >6.59%</td>\n",
       "      <td id=\"T_d5ff4_row15_col4\" class=\"data row15 col4\" >5.73%</td>\n",
       "      <td id=\"T_d5ff4_row15_col5\" class=\"data row15 col5\" >5.80%</td>\n",
       "      <td id=\"T_d5ff4_row15_col6\" class=\"data row15 col6\" >0.23%</td>\n",
       "      <td id=\"T_d5ff4_row15_col7\" class=\"data row15 col7\" >21.98%</td>\n",
       "      <td id=\"T_d5ff4_row15_col8\" class=\"data row15 col8\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row15_col9\" class=\"data row15 col9\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row15_col10\" class=\"data row15 col10\" >0.59%</td>\n",
       "      <td id=\"T_d5ff4_row15_col11\" class=\"data row15 col11\" >0.66%</td>\n",
       "      <td id=\"T_d5ff4_row15_col12\" class=\"data row15 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row16\" class=\"row_heading level0 row16\" >PCAR</th>\n",
       "      <td id=\"T_d5ff4_row16_col0\" class=\"data row16 col0\" >0.20%</td>\n",
       "      <td id=\"T_d5ff4_row16_col1\" class=\"data row16 col1\" >0.33%</td>\n",
       "      <td id=\"T_d5ff4_row16_col2\" class=\"data row16 col2\" >0.39%</td>\n",
       "      <td id=\"T_d5ff4_row16_col3\" class=\"data row16 col3\" >0.64%</td>\n",
       "      <td id=\"T_d5ff4_row16_col4\" class=\"data row16 col4\" >0.35%</td>\n",
       "      <td id=\"T_d5ff4_row16_col5\" class=\"data row16 col5\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row16_col6\" class=\"data row16 col6\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row16_col7\" class=\"data row16 col7\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row16_col8\" class=\"data row16 col8\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row16_col9\" class=\"data row16 col9\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row16_col10\" class=\"data row16 col10\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row16_col11\" class=\"data row16 col11\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row16_col12\" class=\"data row16 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row17\" class=\"row_heading level0 row17\" >PSA</th>\n",
       "      <td id=\"T_d5ff4_row17_col0\" class=\"data row17 col0\" >12.03%</td>\n",
       "      <td id=\"T_d5ff4_row17_col1\" class=\"data row17 col1\" >9.96%</td>\n",
       "      <td id=\"T_d5ff4_row17_col2\" class=\"data row17 col2\" >13.36%</td>\n",
       "      <td id=\"T_d5ff4_row17_col3\" class=\"data row17 col3\" >8.81%</td>\n",
       "      <td id=\"T_d5ff4_row17_col4\" class=\"data row17 col4\" >12.23%</td>\n",
       "      <td id=\"T_d5ff4_row17_col5\" class=\"data row17 col5\" >13.79%</td>\n",
       "      <td id=\"T_d5ff4_row17_col6\" class=\"data row17 col6\" >20.11%</td>\n",
       "      <td id=\"T_d5ff4_row17_col7\" class=\"data row17 col7\" >46.88%</td>\n",
       "      <td id=\"T_d5ff4_row17_col8\" class=\"data row17 col8\" >27.09%</td>\n",
       "      <td id=\"T_d5ff4_row17_col9\" class=\"data row17 col9\" >5.51%</td>\n",
       "      <td id=\"T_d5ff4_row17_col10\" class=\"data row17 col10\" >15.60%</td>\n",
       "      <td id=\"T_d5ff4_row17_col11\" class=\"data row17 col11\" >20.54%</td>\n",
       "      <td id=\"T_d5ff4_row17_col12\" class=\"data row17 col12\" >10.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row18\" class=\"row_heading level0 row18\" >SEE</th>\n",
       "      <td id=\"T_d5ff4_row18_col0\" class=\"data row18 col0\" >2.29%</td>\n",
       "      <td id=\"T_d5ff4_row18_col1\" class=\"data row18 col1\" >2.56%</td>\n",
       "      <td id=\"T_d5ff4_row18_col2\" class=\"data row18 col2\" >1.68%</td>\n",
       "      <td id=\"T_d5ff4_row18_col3\" class=\"data row18 col3\" >1.71%</td>\n",
       "      <td id=\"T_d5ff4_row18_col4\" class=\"data row18 col4\" >1.26%</td>\n",
       "      <td id=\"T_d5ff4_row18_col5\" class=\"data row18 col5\" >0.28%</td>\n",
       "      <td id=\"T_d5ff4_row18_col6\" class=\"data row18 col6\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row18_col7\" class=\"data row18 col7\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row18_col8\" class=\"data row18 col8\" >1.68%</td>\n",
       "      <td id=\"T_d5ff4_row18_col9\" class=\"data row18 col9\" >0.82%</td>\n",
       "      <td id=\"T_d5ff4_row18_col10\" class=\"data row18 col10\" >0.53%</td>\n",
       "      <td id=\"T_d5ff4_row18_col11\" class=\"data row18 col11\" >1.67%</td>\n",
       "      <td id=\"T_d5ff4_row18_col12\" class=\"data row18 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row19\" class=\"row_heading level0 row19\" >T</th>\n",
       "      <td id=\"T_d5ff4_row19_col0\" class=\"data row19 col0\" >4.89%</td>\n",
       "      <td id=\"T_d5ff4_row19_col1\" class=\"data row19 col1\" >4.10%</td>\n",
       "      <td id=\"T_d5ff4_row19_col2\" class=\"data row19 col2\" >3.50%</td>\n",
       "      <td id=\"T_d5ff4_row19_col3\" class=\"data row19 col3\" >3.95%</td>\n",
       "      <td id=\"T_d5ff4_row19_col4\" class=\"data row19 col4\" >3.44%</td>\n",
       "      <td id=\"T_d5ff4_row19_col5\" class=\"data row19 col5\" >4.12%</td>\n",
       "      <td id=\"T_d5ff4_row19_col6\" class=\"data row19 col6\" >0.94%</td>\n",
       "      <td id=\"T_d5ff4_row19_col7\" class=\"data row19 col7\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row19_col8\" class=\"data row19 col8\" >1.61%</td>\n",
       "      <td id=\"T_d5ff4_row19_col9\" class=\"data row19 col9\" >3.00%</td>\n",
       "      <td id=\"T_d5ff4_row19_col10\" class=\"data row19 col10\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row19_col11\" class=\"data row19 col11\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row19_col12\" class=\"data row19 col12\" >0.11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row20\" class=\"row_heading level0 row20\" >TGT</th>\n",
       "      <td id=\"T_d5ff4_row20_col0\" class=\"data row20 col0\" >3.03%</td>\n",
       "      <td id=\"T_d5ff4_row20_col1\" class=\"data row20 col1\" >2.57%</td>\n",
       "      <td id=\"T_d5ff4_row20_col2\" class=\"data row20 col2\" >3.62%</td>\n",
       "      <td id=\"T_d5ff4_row20_col3\" class=\"data row20 col3\" >3.00%</td>\n",
       "      <td id=\"T_d5ff4_row20_col4\" class=\"data row20 col4\" >3.90%</td>\n",
       "      <td id=\"T_d5ff4_row20_col5\" class=\"data row20 col5\" >5.13%</td>\n",
       "      <td id=\"T_d5ff4_row20_col6\" class=\"data row20 col6\" >0.65%</td>\n",
       "      <td id=\"T_d5ff4_row20_col7\" class=\"data row20 col7\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row20_col8\" class=\"data row20 col8\" >1.09%</td>\n",
       "      <td id=\"T_d5ff4_row20_col9\" class=\"data row20 col9\" >1.06%</td>\n",
       "      <td id=\"T_d5ff4_row20_col10\" class=\"data row20 col10\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row20_col11\" class=\"data row20 col11\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row20_col12\" class=\"data row20 col12\" >0.04%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row21\" class=\"row_heading level0 row21\" >TMO</th>\n",
       "      <td id=\"T_d5ff4_row21_col0\" class=\"data row21 col0\" >1.11%</td>\n",
       "      <td id=\"T_d5ff4_row21_col1\" class=\"data row21 col1\" >0.69%</td>\n",
       "      <td id=\"T_d5ff4_row21_col2\" class=\"data row21 col2\" >1.37%</td>\n",
       "      <td id=\"T_d5ff4_row21_col3\" class=\"data row21 col3\" >0.80%</td>\n",
       "      <td id=\"T_d5ff4_row21_col4\" class=\"data row21 col4\" >1.64%</td>\n",
       "      <td id=\"T_d5ff4_row21_col5\" class=\"data row21 col5\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row21_col6\" class=\"data row21 col6\" >2.93%</td>\n",
       "      <td id=\"T_d5ff4_row21_col7\" class=\"data row21 col7\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row21_col8\" class=\"data row21 col8\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row21_col9\" class=\"data row21 col9\" >6.57%</td>\n",
       "      <td id=\"T_d5ff4_row21_col10\" class=\"data row21 col10\" >4.65%</td>\n",
       "      <td id=\"T_d5ff4_row21_col11\" class=\"data row21 col11\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row21_col12\" class=\"data row21 col12\" >8.48%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row22\" class=\"row_heading level0 row22\" >TXT</th>\n",
       "      <td id=\"T_d5ff4_row22_col0\" class=\"data row22 col0\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row22_col1\" class=\"data row22 col1\" >0.46%</td>\n",
       "      <td id=\"T_d5ff4_row22_col2\" class=\"data row22 col2\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row22_col3\" class=\"data row22 col3\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row22_col4\" class=\"data row22 col4\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row22_col5\" class=\"data row22 col5\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row22_col6\" class=\"data row22 col6\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row22_col7\" class=\"data row22 col7\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row22_col8\" class=\"data row22 col8\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row22_col9\" class=\"data row22 col9\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row22_col10\" class=\"data row22 col10\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row22_col11\" class=\"data row22 col11\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row22_col12\" class=\"data row22 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row23\" class=\"row_heading level0 row23\" >VZ</th>\n",
       "      <td id=\"T_d5ff4_row23_col0\" class=\"data row23 col0\" >8.62%</td>\n",
       "      <td id=\"T_d5ff4_row23_col1\" class=\"data row23 col1\" >7.14%</td>\n",
       "      <td id=\"T_d5ff4_row23_col2\" class=\"data row23 col2\" >9.18%</td>\n",
       "      <td id=\"T_d5ff4_row23_col3\" class=\"data row23 col3\" >7.13%</td>\n",
       "      <td id=\"T_d5ff4_row23_col4\" class=\"data row23 col4\" >9.46%</td>\n",
       "      <td id=\"T_d5ff4_row23_col5\" class=\"data row23 col5\" >13.15%</td>\n",
       "      <td id=\"T_d5ff4_row23_col6\" class=\"data row23 col6\" >2.02%</td>\n",
       "      <td id=\"T_d5ff4_row23_col7\" class=\"data row23 col7\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row23_col8\" class=\"data row23 col8\" >1.93%</td>\n",
       "      <td id=\"T_d5ff4_row23_col9\" class=\"data row23 col9\" >3.25%</td>\n",
       "      <td id=\"T_d5ff4_row23_col10\" class=\"data row23 col10\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row23_col11\" class=\"data row23 col11\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row23_col12\" class=\"data row23 col12\" >0.10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d5ff4_level0_row24\" class=\"row_heading level0 row24\" >ZION</th>\n",
       "      <td id=\"T_d5ff4_row24_col0\" class=\"data row24 col0\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row24_col1\" class=\"data row24 col1\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row24_col2\" class=\"data row24 col2\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row24_col3\" class=\"data row24 col3\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row24_col4\" class=\"data row24 col4\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row24_col5\" class=\"data row24 col5\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row24_col6\" class=\"data row24 col6\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row24_col7\" class=\"data row24 col7\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row24_col8\" class=\"data row24 col8\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row24_col9\" class=\"data row24 col9\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row24_col10\" class=\"data row24 col10\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row24_col11\" class=\"data row24 col11\" >0.00%</td>\n",
       "      <td id=\"T_d5ff4_row24_col12\" class=\"data row24 col12\" >0.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x3187e0750>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_s.style.format(\"{:.2%}\").background_gradient(cmap='YlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting a comparison of assets weights for each portfolio\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_figwidth(14)\n",
    "fig.set_figheight(6)\n",
    "ax = fig.subplots(nrows=1, ncols=1)\n",
    "\n",
    "w_s.plot.bar(ax=ax)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
